Prevalence of Vitamin D Insufficiency in Qatar: A Systematic Review
Bibliographic record
Abstract
Qatar has a high burden of chronic diseases including obesity, cardiovascular disease and type 2 diabetes mellitus. Low serum vitamin D levels have been implicated in the development and progression of a range of these chronic conditions. The prevalence of vitamin D insufficiency or deficiency in the general population of Qatar has still not been investigated. The aim of this study was to carry out a systematic review of published studies documenting the prevalence of vitamin D insufficiency or deficiency in the Qatari population. A search strategy was developed for online databases (PubMed, Ovid MEDLINE, Embase and Embase Classic) between 1980 to the last week of August 2012, and bibliographies of the included studies were further searched for additional reports. Search terms used were QATAR and VITAMIN D. Studies reporting the serum levels of vitamin D in several Qatari sub-populations were identified. Weighted-average vitamin D serum levels and prevalence of low vitamin D status (<75 nmol/L) were calculated. Subgroup analysis was carried out by age. The quality of each study was evaluated according to four criteria: national representativeness, representation of males and females, the sample size, and the sampling protocol. A total of 16 relevant publications were identified, and 8 of these (reporting from 7 unique studies) met our inclusion and exclusion criteria with a total number of 1,699 Qatari subjects. The pooled sample size weighted-average vitamin D concentration (±SD) was 45.3±14.3 nmol/L (95% CI: 44.6-46.0; range 29.2-66.9 nmol/L). The weighted-average prevalence of low vitamin D status was 90.4% (95% CI: 90.1-91.0; range 83%-91%). Age was inversely correlated with vitamin D levels and directly with its insufficiency/deficiency prevalence. There have only been a few studies on the prevalence of low vitamin D in Qatar a very high prevalence of vitamin D insufficiency/deficiency in Qatar that increases with age has been suggested. The present report underlines the need to develop a nationally representative study to further evaluate vitamin D status in Qatar. Given the growing evidence of the role of vitamin D in chronic disease, this study could help develop public health strategies for disease prevention in Qatar.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".